where \( \mathcal {N}(\mu , \Sigma ) \)  denotes the normal distribution on \( \mathbb {R}^n \)  with mean \( \mu \)  and covariance \( \Sigma \) . \( \beta , \epsilon \in \mathbb {R}^+ \)  are appropriately chosen scaling parameters, and \( K \)  denotes the total number of steps.
To aid the convergence of the Markov chains, we further follow the idea of persistent chains [1]} and use a buffer in which the states of the chains persist throughout parameter updates.
